Rice crops monitoring in the Mekong river delta using combined ERS and RADARSAT synthetic aperture radar

Liew, Soo Chin; Chen, P.; Kam, S. P.; Tuong, T. P.; Minh, Võ Quang; Lim, H. S.

doi:10.1109/igarss.1998.702338

Cited by 7 publications

(7 citation statements)

References 3 publications

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“…Time-series of spaceborne SAR sensors have been used to map rice growing regions since 1991 with the availability of data from the European Remote Sensing Satellite 1 (ERS-1) in Canada (McNairn and Brisco 2004;Jia et al 2013), India (Patel et al 1995;Chakraborty, Panigrahy, and Sharma 1997;Panigrahy et al 1997;Premalatha and Nageswara Rao 1994), Indonesia (Le Toan et al 1997), Japan (Le Toan et al 1997Chiba 1995, 1997), Thailand (Aschbacher et al 1995) and Vietnam (Liew et al 1998a). These initial studies laid important ground work in regards to method development, which was successfully transferred to succeeding SAR sensors.…”

Section: Introductionmentioning

confidence: 99%

“…These initial studies laid important ground work in regards to method development, which was successfully transferred to succeeding SAR sensors. Radarsat-1 had a shorter repeat cycle and has been used to map rice areas with horizontal co-polarised time-series in India (Choudhury and Chakraborty 2006;Chakraborty et al 2005;Panigrahy et al 1999), Indonesia (Ribbes 1999), China (Li et al 2003;Shao et al 2001;Wu et al 2011;Zhang et al 2016) and Japan (Yonezawa et al 2012) as well as the first multi-sensor studies in conjunction with ERS-1/2 data (Liew et al 1998a). Envisat ASARs (Advanced Synthetic Aperture Radar) Wide-Swath mode enabled rice mapping at regional scales (Bouvet and Le Toan 2011;Bouvet, Le Toan, and Lam-Dao 2009;Nguyen et al 2015) and X-band sensors TerraSAR-X and COSMO-SkyMed (Constellation of Small Satellites for Mediterranean basin Observation) enabled paddy rice mapping at unprecedented spatial resolution (Lopez-Sanchez, Ballester-Berman, and Hajnsek 2011;Koppe et al 2013;Nguyen et al 2012;Nelson et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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Mapping rice areas with Sentinel-1 time series and superpixel segmentation

Clauss

Ottinger

Kuenzer

2017

International Journal of Remote Sensing

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Rice is the single most important crop for food security in Asia. Knowledge about the distribution of rice fields is also relevant in the context of greenhouse relevant methane emissions, disease transmission and water resource management. Copernicus Sentinel-1 provides the first openly available archive of C-band SAR (Synthetic Aperture Radar) data at high spatial and temporal resolution. We developed one of the first methods that shows the potential of this data for accurate and timely mapping of rice growing areas. We used superpixel segmentation to create spatially averaged backscatter time-series, which are robust to speckle and reduce the amount of data to process. This method has been applied to six study sites in different rice growing regions of the world and achieved an average overall accuracy of 0.83.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping rice areas with Sentinel-1 time series and superpixel segmentation

Clauss

Ottinger

Kuenzer

2017

International Journal of Remote Sensing

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“…Under these circumstances, the synthetic aperture radar (SAR) has been considered the most effective sensor in detecting flood inundation areas. In the past, the images taken from various satellites such as RADARSAT, JERS‐1, ERS‐1/2 and ENVISAT have also been used to detect inundated areas in a number of ways for example, through the rule‐based analysis of polarized data (Hirose et al , 2001; Henry et al , 2003), object‐oriented classification (Heremans et al , 2005), filtering of microware data (Ishitsuka et al , 2003), high temporal detection (Laugier et al , 1997), rule‐based analysis of SAR data (Nguyen and Bui, 2001; Wang, 2002, 2004) and combining both SAR and RADARSAT images (Liew et al , 1998).…”

Section: Introductionmentioning

confidence: 99%

Flood inundation map of Bangladesh using MODIS time‐series images

Islam¹,

Bala²,

Haque³

2010

J Flood Risk Management

151

102

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The detection of the spatio-temporal extent of inundation resulting from the floods in 2004 and 2007 in Bangladesh has been studied using time-series MODIS surface reflectance data. Flood inundation maps were developed from vegetation and land water surface indices derived using surface reflectance. The inundation map developed using MODIS data was compared with a corresponding RADAR-SAT image, where both images refer to the satellite-based remote-sensing data. The estimates show a strong correlation with the inundation area derived from the RADARSAT products (coefficient of determination of R 2 : 0.96). The paper shows that it is possible to study flood dynamics by assessing inundation and recession patterns and to perform flood assessments similar to the high-resolution (50 m) microwave satellite, RADARSAT-based flood assessments using products derived from MODIS 500 m imagery. MODIS has advantages over microwave satellite because it has a high observational frequency and these data are available free of cost. We have concluded that this is a useful method to assess the extent of the temporal floods in the People's Republic of Bangladesh. Figure 8 Temporal changes of flood and mixture pixels for four types of paddy fields (single-cropped irrigated rice, single-cropped rain fed rice, doublecropped irrigated rice and triple-cropped irrigated rice), forest and settlement areas.

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“…Methods developed for ERS-1 SAR data have been advanced and successfully transferred to RADARSAT-1 acquisitions at C-band with horizontal co-polarization and a 24-day repeat cycle [19,[34][35][36][37][38][39]. Joint analysis of ERS-1/2 and RADARSAT-1 data showed how polarization and incidence angle influence backscatter from paddy rice [40]. Comparative studies showed that C-, L-and X-band can be utilized for the multi-temporal method [41,42].…”

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confidence: 99%

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

et al. 2015

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Abstract:Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM (Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double-and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R² of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions.

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Rice crops monitoring in the Mekong river delta using combined ERS and RADARSAT synthetic aperture radar

Cited by 7 publications

References 3 publications

Mapping rice areas with Sentinel-1 time series and superpixel segmentation

Mapping rice areas with Sentinel-1 time series and superpixel segmentation

Flood inundation map of Bangladesh using MODIS time‐series images

Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data

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